Adaptive separation of unknown narrowband and broadband time series

Kirsteins, Ivars; Mehta, Sangeeta; Fay, J.

doi:10.1109/icassp.1998.681665

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…These methods are based on three aspects of signal processing technologies including time domain analysis, frequency domain analysis, and time-frequency analysis. Most of previous works on automatic recognition of ship radiated noise have dealt with extracting features from the frequency domain, utilizing fast Fourier transform (FFT) power spectrum [6][7][8][9][10]. But it is well known that timefrequency domain analysis has much more advantages for ship radiated noises which are nonstationary signals.…”

Section: Introductionmentioning

confidence: 99%

Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition

Yan

Sun

Cheng

et al. 2017

Shock and Vibration

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Under the complex oceanic environment, robust and effective feature extraction is the key issue of ship radiated noise recognition. Since traditional feature extraction methods are susceptible to the inevitable environmental noise, the type of vessels, and the speed of ships, the recognition accuracy will degrade significantly. Hence, we propose a robust time-frequency analysis method which combines resonance-based sparse signal decomposition (RSSD) and Hilbert marginal spectrum (HMS) analysis. First, the observed signals are decomposed into high resonance component, low resonance component, and residual component by RSSD, which is a nonlinear signal analysis method based not on frequency or scale but on resonance. High resonance component is multiple simultaneous sustained oscillations, low resonance component is nonoscillatory transients, and residual component is white Gaussian noises. According to the low-frequency periodic oscillatory characteristic of ship radiated noise, high resonance component is the purified ship radiated noise. RSSD is suited to noise suppression for low-frequency oscillation signals. Second, HMS of high resonance component is extracted by Hilbert-Huang transform (HHT) as the feature vector. Finally, support vector machine (SVM) is adopted as a classifier. Real audio recordings are employed in the experiments under different signal-to-noise ratios (SNRs). The experimental results indicate that the proposed method has a better recognition performance than the traditional method under different SNRs.

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Section: Introductionmentioning

confidence: 99%

Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition

Yan

Sun

Cheng

et al. 2017

Shock and Vibration

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Application of Multitaper Analysis to wireless communications problems

Primak

Sejdić

2008

2008 First International Symposium on Applied Sciences on Biomedical and Communication Technologies

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Adaptive whitening of ambient ocean noise with narrowband signal preservation

Hollmann

Stevenson

2016

The Journal of the Acoustical Society of America

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Passive underwater listening devices are often deployed to listen for narrowband signals of interest in time-varying background ocean noise. Such tonals are generated mechanically by ships, submarines, and machines, or acoustically by aquatic wildlife. Quantization of the sensor data for storage or low bit-rate transmission adds white noise which can overwhelm weak narrowband signals if the background noise is sufficiently colored. Whitening the background noise prior to quantization can reduce the detrimental effects, but the whitening process must preserve any tonals in the signal for maximum effectiveness. Existing adaptive whitening techniques make no effort to avoid suppressing tonals in the whitening process, while existing spectral separation methods fail to whiten background noise. The proposed methods perform adaptive whitening of background ambient noise while preserving narrowband tones at their original signal-to-noise ratios. The proposed methods are shown to outperform combinations of existing partial solutions both subjectively and by evaluating the objective criteria introduced. The stability and convergence properties of the proposed algorithms match or surpass those of existing well-known adaptive algorithms.

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Adaptive separation of unknown narrowband and broadband time series

Cited by 4 publications

References 6 publications

Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition

Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition

Application of Multitaper Analysis to wireless communications problems

Adaptive whitening of ambient ocean noise with narrowband signal preservation

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